The Anomaly-Aware model is designed to explicitly incorporate extracted anomalies \(\mathbf {a}^{(k)}\)  and extreme event covariates \(\mathbf {e}^{(k)}\)  into the prediction.
As these features are rare in the whole time series, feeding them directly into a regular RNN like LSTM [1]} can be potentially ignored during the training of the model. Note that the extracted anomalies and previously experienced external events hold valuable information regarding the effect of extreme events that should be handled carefully.
